Abstract: A method for training a model to detect lane markings. The method includes: providing images that are specific to a recording by at least one sensor of a vehicle, and in which the lane markings are mapped; providing ground truth data specific to a geometry of the lane markings in the provided images; training the model on the basis of the provided images and ground truth data for a three-dimensional modeling of the geometry of the lane markings based on a parameterization of a continuous curve.

Abstract: A method for determining at least one anchor for an anchor-based lane line recognition and/or roadway marking recognition in a digital image representation on the basis of sensor data that are obtained from at least one surroundings sensor of a system. The method includes at least the following steps: a) receiving a digital image representation, b) setting at least one row or one column of possible anchors in at least one area of the digital image representation, the row or column of possible anchors being situated at a distance from at least the upper and lower or left and right edge of the area of the digital image representation.

Abstract: A system to predict calibration values for a vehicle. The system is configured to receive a plurality of training data sets for a component of the vehicle. Each of the plurality of training data sets includes one or more training inputs and one or more corresponding training outputs. The system is further configured to automatically develop a prediction model based on the plurality of training data sets. The system is further configured to receive an input data set and determine, using the prediction model, a predicted calibration value based on the input data set. The system is further configured to transmit the predicted calibration value to an electronic control unit of the vehicle.

Abstract: A system to predict calibration values for a vehicle. The system is configured to receive a plurality of training data sets for a component of the vehicle. Each of the plurality of training data sets includes one or more training inputs and one or more corresponding training outputs. The system is further configured to automatically develop a prediction model based on the plurality of training data sets. The system is further configured to receive an input data set and determine, using the prediction model, a predicted calibration value based on the input data set. The system is further configured to transmit the predicted calibration value to an electronic control unit of the vehicle.